GoCAV Presentation
The GoCAV project is designed to minimize risks and accelerate the development of full-size autonomous vehicles by bridging the gap between simulation and real-world application.
With an advanced AI system and sensors like cameras, Lidar, and ultrasonics, GoCAV integrates these technologies with traditional vehicle controls—steering, throttle, and brakes—connected to the vehicle’s CPU. Supported by Real Time Kinematics GPS and a safety-oriented kill switch, the system’s design combats electromagnetic interference with high-quality cables and strategic component placement. Further enhancements include a CAN-FD network for robust microcontroller-to-CPU communication and an integrated control panel for real-time diagnostics and engine monitoring.
GoCAV Camera Evaluation
The GoCAV camera system integrates advanced vision technologies to accurately detect and interpret the environment. Coupled with LiDAR and ultrasonic sensors, the camera trials evaluate the robustness of object detection and obstacle avoidance in real-world scenarios. These trials also test the system's ability to adapt to varying lighting conditions and environmental challenges, ensuring optimal sensor fusion and reliability. By continuously refining the camera performance, the trials aim to push the boundaries of autonomous vehicle vision capabilities, paving the way for safer and more reliable autonomous systems.
GoCAV Platform Evaluation
The GoCAV Platform bridges advanced AI systems with real-world applications, offering real-time visualization of vehicle movement, sensor performance, and diagnostics. Connected with the vehicle's CPU and CAN-FD network ensures rapid issue detection and resolution, supporting smooth transitions from simulations to test track evaluations.
The Platform enables monitoring and adaptation to diverse conditions, accelerating autonomous feature development and facilitating integration with new technology.
GoCAV GPS Trials
The GoCAV system leverages Real-Time Kinematics (RTK) GPS to ensure precise positioning, achieving centimeter-level accuracy critical for autonomous navigation. This level of precision enhances the system's ability to operate reliably in complex environments and provides a robust framework for developing advanced path-planning algorithms. By transitioning from controlled simulations to real-world test track evaluations, the GPS trials validate the system's reliability under diverse conditions, including potential electromagnetic interference. These trials are pivotal in reducing testing time while enhancing safety and performance in pilot tests.